Title :
An Improved Differential Evolution Alogorithm for Optimization
Author :
Huibin, Jin ; Mingguang, Liu
Author_Institution :
Res. Inst. of Civil Aviation Safety, Civil Aviation Univ. of China, Tianjin, China
Abstract :
Differential Evolution (DE) is an efficient approach capable of handling non-differentiable, non-linear and multi-model objective functions. However, in convergence speed and global optimization, there is still much room for DE to be improved. In this paper, double best mutation operation and chaos Differential Evolution are proposed to improve DE algorithmpsilas optimized performance. The simulated cases show modified differential evolution algorithm has rapid convergence speed and strong steadiness.
Keywords :
chaos; convergence; evolutionary computation; particle swarm optimisation; chaos differential evolution; convergence speed; double best mutation operation; particle swarm optimization; Automatic control; Automation; Chaos; Control systems; Convergence; Evolution (biology); Evolutionary computation; Genetic mutations; Nonlinear control systems; Stochastic processes; chaos differential evolution; differential evolution; double best mutation; particle swarm optimization;
Conference_Titel :
Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-0-7695-3728-3
DOI :
10.1109/CASE.2009.116
